Data Analysis Agent System Prompt
Generates an analysis agent prompt that states its assumptions, checks data quality before concluding, refuses causal claims the data cannot support, and shows its working.
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Running it, start to finish
- Describe the data, including what one row represents.
- Run all three test questions before relying on it.
- Check the assumptions section on the first few real answers.
What you get back
The output this produces, every time.
- Runs data quality checks before every analysis and stops when one fails.
- Refuses causal claims the data cannot support, which is where analyses usually mislead.
- Reports sample sizes per figure, flagging cuts too small to conclude from.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Describe the data's grain. What one row represents determines whether any aggregate is correct, and it is the thing most often left unstated.
- Run the causation test question. It checks the one instruction that most often gets ignored under the pressure to give a clean answer.
- Keep the show-your-working rule. An answer that cannot be checked has to be trusted, and analysis is exactly where trust should not be required.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.